Fall supplemental feeding increases population growth rate of an endangered caribou herd
Bibliographic record
Abstract
Most woodland caribou (Rangifer tarandus caribou) populations are declining primarily because of unsustainable predation resulting from habitat-mediated apparent competition. Wolf (Canis lupus) reduction is an effective recovery option because it addresses the direct effect of predation. We considered the possibility that the indirect effects of predation might also affect caribou population dynamics by adversely affecting summer foraging behaviour. If spring and/or summer nutrition was inadequate, then supplemental feeding in fall might compensate for that limitation and contribute to population growth. Improved nutrition and therefore body condition going into winter could increase adult survival and lead to improved reproductive success the next spring. To test that hypothesis, we fed high-quality food pellets to free-ranging caribou in the Kennedy Siding caribou herd each fall for six years, starting in 2014, to see if population growth rate increased. Beginning in winter 2015–16, the Province of British Columbia began a concurrent annual program to promote caribou population increase by attempting to remove most wolves within the Kennedy Siding and the adjacent caribou herds’ ranges. To evaluate the impact of feeding, we compared lambdas before and after feeding began, and to the population trend in the adjacent Quintette herd over the subsequent four years. Supplemental feeding appeared to have an incremental effect on population growth. Population growth of the Kennedy Siding herd was higher in the year after feeding began (λ = 1.06) compared to previous years (λ = 0.91) and to the untreated Quintette herd (λ = 0.95). Average annual growth rate of the Kennedy Siding herd over the subsequent four years, where both feeding and wolf reduction occurred concurrently, was higher than in the Quintette herd where the only management action in those years was wolf reduction (λ = 1.16 vs. λ = 1.08). The higher growth rate of the Kennedy Siding herd was due to higher female survival (96.2%/yr vs. 88.9%/yr). Many caribou were in relatively poor condition in the fall. Consumption of supplemental food probably improved their nutritional status which ultimately led to population growth. Further feeding experiments on other caribou herds using an adaptive management approach would verify the effect of feeding as a population recovery tool. Our results support the recommendation that multiple management actions should be implemented to improve recovery prospects for caribou.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".